Data, AI, and Cloud Courses

Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

Discover the fundamental concepts of object-oriented programming (OOP), building custom classes and objects!

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Learn to use best practices to write maintainable, reusable, complex functions with good documentation.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Learn how to clean and prepare your data for machine learning!

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Learn how to work with dates and times in Python.

Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.

Get to grips with the foundational components of LangChain agents and build custom chat agents.

Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.

Discover how the Pinecone vector database is revolutionizing AI application development!

Learn to retrieve and parse information from the internet using the Python library scrapy.

In this course youll learn the basics of working with time series data.

Learn how to manipulate and visualize categorical data using pandas and seaborn.

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

Master text analysis with essential NLP techniques from preprocessing to advanced transformer models.

Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

Build powerful multi-agent systems by applying emerging agentic design patterns in the LangGraph framework.

Learn to process, transform, and manipulate images at your will.

Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.

Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.

Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.

Create new features to improve the performance of your Machine Learning models.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.